We also evaluated the impact of the TS-VAD diarization system on downstream ASR performance. For this, we built a hybrid HMM-DNN system following the Kaldi [1]} Librispeech recipe. In Table REF , we present the ASR performance in terms of concatenated minimum-permutation word error rates (cpWERs) [2]}. The cpWER is computed by concatenating all the utterances of a speaker in the reference and hypothesis, scoring all speaker pairs, and then finding the speaker permutation that minimizes the total WER. We compared the cpWERs obtained using 3 different segmentation methods: oracle, SC, and 8-spk TS-VAD, where the 8-spk TS-VAD model was initialized using the SC output. We found that the going from SC to TS-VAD resulted in a significant cpWER improvement from 32.2% to 25.8% for LibriCSS-8spk set. This was very close to the cpWER obtained using oracle segments, which was 23.1%. Similar improvements were observed using the 8spk TS-VAD on both LibriCSS-2spk and LibriCSS-5spk compared with the SC system. However, the cpWER on such conditions was still high, indicating that better ASR models, or an external speech separation module may be required to satisfactorily transcribe such recordings.
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